JP4435307B2 - Online control of chemical process plant - Google Patents
Online control of chemical process plant Download PDFInfo
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- JP4435307B2 JP4435307B2 JP53015698A JP53015698A JP4435307B2 JP 4435307 B2 JP4435307 B2 JP 4435307B2 JP 53015698 A JP53015698 A JP 53015698A JP 53015698 A JP53015698 A JP 53015698A JP 4435307 B2 JP4435307 B2 JP 4435307B2
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Images
Classifications
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- G—PHYSICS
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- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D21/00—Control of chemical or physico-chemical variables, e.g. pH value
- G05D21/02—Control of chemical or physico-chemical variables, e.g. pH value characterised by the use of electric means
-
- C—CHEMISTRY; METALLURGY
- C08—ORGANIC MACROMOLECULAR COMPOUNDS; THEIR PREPARATION OR CHEMICAL WORKING-UP; COMPOSITIONS BASED THEREON
- C08F—MACROMOLECULAR COMPOUNDS OBTAINED BY REACTIONS ONLY INVOLVING CARBON-TO-CARBON UNSATURATED BONDS
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- C08F210/04—Monomers containing three or four carbon atoms
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
- G01J3/00—Spectrometry; Spectrophotometry; Monochromators; Measuring colours
- G01J3/28—Investigating the spectrum
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
- G01J3/00—Spectrometry; Spectrophotometry; Monochromators; Measuring colours
- G01J3/28—Investigating the spectrum
- G01J3/45—Interferometric spectrometry
- G01J3/453—Interferometric spectrometry by correlation of the amplitudes
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
- G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
- G01N21/3563—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light for analysing solids; Preparation of samples therefor
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
- G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
- G01N21/359—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light using near infrared light
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
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- G01N21/84—Systems specially adapted for particular applications
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- C—CHEMISTRY; METALLURGY
- C08—ORGANIC MACROMOLECULAR COMPOUNDS; THEIR PREPARATION OR CHEMICAL WORKING-UP; COMPOSITIONS BASED THEREON
- C08F—MACROMOLECULAR COMPOUNDS OBTAINED BY REACTIONS ONLY INVOLVING CARBON-TO-CARBON UNSATURATED BONDS
- C08F2400/00—Characteristics for processes of polymerization
- C08F2400/02—Control or adjustment of polymerization parameters
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- G—PHYSICS
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- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
- G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
- G01N2021/3595—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light using FTIR
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/84—Systems specially adapted for particular applications
- G01N2021/8411—Application to online plant, process monitoring
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N2201/00—Features of devices classified in G01N21/00
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- G01N2201/129—Using chemometrical methods
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- Physics & Mathematics (AREA)
- Spectroscopy & Molecular Physics (AREA)
- General Physics & Mathematics (AREA)
- Chemical & Material Sciences (AREA)
- Health & Medical Sciences (AREA)
- Biochemistry (AREA)
- General Health & Medical Sciences (AREA)
- Analytical Chemistry (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Life Sciences & Earth Sciences (AREA)
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- Engineering & Computer Science (AREA)
- Chemical Kinetics & Catalysis (AREA)
- Medicinal Chemistry (AREA)
- Polymers & Plastics (AREA)
- Organic Chemistry (AREA)
- Investigating Or Analysing Materials By Optical Means (AREA)
- Control Of Non-Electrical Variables (AREA)
- Investigating, Analyzing Materials By Fluorescence Or Luminescence (AREA)
Description
発明の分野
本発明は、化学プラント及び化学プラントでの化学的プロセスの制御方法に関する。より詳しくは、本発明は、イソオレフィンコポリマー及びマルチオレフィン、特にブチルゴム、の重合又はハロゲン化工程の間において、ムーニー粘度、ポリマーの不飽和度、コモノマーの組込み、ハロゲン含有量、分子量、及び分子量分布を制御する方法に関する。
関連技術の記載
不活性溶媒又は希釈剤のような媒質中でのオレフィンの重合を制御するための卓越した方法は、単一の変数、「ムーニー粘度」、を計算するために、媒質中のポリマー濃度及びポリマー溶液の粘度を測定することを含む。次に、ムーニー粘度は、プロセス全体を制御する、単一の変数として使用される。
「単一の変数」プロセス制御は、所望の生成物の品質が、ただ一つの変数に直接比例する場合、非常に有効である。「単一の変数」プロセス制御は、2つ以上の変数が生成物の品質に直接関連する場合、あまり有効でない。例えば、ブチルゴムの品質は、加工の間のポリマー溶液内のムーニー粘度及び分子量分布の両方に直接関連する。
ポリマーの分子量分布及びムーニー粘度に基づく、ブチルゴムの製造のための更なるプロセス制御を提供するために、種々の試みがなされてきた。不運なことに、これまでの方法は、効率が悪いか、又はムーニー粘度同様分子量分布に基づいてプロセスを効果的に制御するためには不十分な包括的なデータに基づいていた。
ムーニー粘度及びポリマー分子量の両方をプロセス制御パラメーターとして使用し、ブチルゴムの製造を制御するための効率的で正確な方法に対する需要が存在する。
発明の要約
本発明は、所望の値Dを有する特性Pを有する生成物を製造する複数の工程を有するプロセス・プラントをオンライン制御するための方法である。この方法では、プロセスでの少なくとも一つの中間工程を代表する一組の較正試料について、一組の測定されたスペクトルを得、較正試料についての測定誤差の影響を除去し、一組の較正試料についての一組の補正されたスペクトルを生じる。各較正試料の補正されたスペクトルを固有スペクトルに関係付けて一組の加重値が決定され、加重値の行列を与える。各較正試料について、最終生成物の特性Pの値が得られる。次に、較正試料についての生成物の特性Pの値を一組の加重値に関係付けて予測モデルが導かれる。次に、プロセスの中間工程での試験試料のスペクトルが測定され、測定誤差が補正される。試験試料についての特性Pの値は、較正試料及び試験試料の補正されたスペクトルに由来する一組の加重値を使用する予測モデルから予測される。予測された値と所望の値との間の差は、プロセスを制御するために使用される。任意に、スペクトルに加えて、測定が行われ得、予測モデル及び予測プロセスの導出に使用され得る。
【図面の簡単な説明】
第1図は、ブチルゴムの重合のためのプラントの簡素化された流れ図である。
第2図は、ブチルゴムのハロゲン化のためのプラントの簡素化された流れ図である。
第3図は、計装及びプロセス制御に使用される装置を記載する。
第4図は、本発明の方法に従って測定された及び予測されたムーニー粘度の比較である。
第5図は、本発明の方法に従って測定された及び予測された不飽和度の比較である。
発明の詳細な説明
本発明は、本発明の好ましい態様を例示する添付の第1図乃至第5図を参照して、最善に理解される。
ブチルゴムの世界生産のバルクは、−100℃乃至−900℃で塩化メチル希釈液中の塩化アルミニウムを使用してイソブチレン及び少量のイソプレンを共重合する沈殿(スラリー)重合法で製造される。ハロゲン化されたブチルゴムは、ブチルゴムを炭化水素溶媒中に溶解し、その溶液をハロゲン元素と接触させることにより、商業的に生産される。
第1図は、スラリー法の重合区画の簡素化された流れ図である。イソブチレン101は、乾燥塔103で乾燥され分別される。水103aは、除去され、イソブチレン、2−ブテン及び高沸点成分を含む画分103bが、イソブチレン精製塔105で精製される。供給原料ブレンド・ドラム109は、25乃至40重量%のイソブチレン105b、0.4乃至1.4重量%のイソプレン107(製造されるブチルゴムの等級による)、及び塩化メチル精製塔111から再循環された塩化メチル111aからなる供給原料をブレンドする。純粋な塩化メチル111bを、30乃至45℃にて粒状塩化アルミニウムの床113を通過させることにより、共開始剤溶液が生成される。この濃縮された溶液113bは、追加の塩化メチルで希釈され、ドラム115に貯蔵される。希釈された混合物は、触媒冷却器117で−100乃至−90℃の温度へ冷却される。冷却された共開始剤117bは、反応器119へ供給される。反応器は、同心列の冷却管に囲まれた中央に存在する垂直なドラフト管を含む。冷却管を通ってスラリーを循環させる、ドラフト管の底部に位置する軸流ポンプにより、反応器が混合される。共重合反応は、発熱反応であり、ポリマー1kg当たり約0.82MJ(350Btu/lb)を放出する。熱は、反応器の管区画を囲むジャケットに液体として供給されている沸騰エチレンに対して交換することにより、除去される。反応器は、重合反応の低温での適切な衝撃強さを有する合金で組み立てられている。第1図に示すように、ブレンドされた供給原料109aは、供給原料冷却器121で冷却され、反応器119へ供給される。反応器119中で生成されたポリマーの特性を制御するために、分枝剤109bが、ブレンドされた供給原料109aへ添加され得る。反応器のアウトプット119aは、ブチルゴム、塩化メチル、及び未反応のモノマーからなる。温かいヘキサン及びヘキサン蒸気125及び冷却剤125bが、反応器出口ライン119a及び溶液ドラム123へ添加され、塩化メチルのほとんど及び未反応のモノマーが気化され、再循環気体凝縮機151へ送られる。ブチルゴムの液体ヘキサン溶液が、セメント・ストリッパー131(ここには、熱いヘキサン蒸気133が添加される)へ供給される。セメント・ストリッパー131の底部からの熱いセメント131aは、ヘキサン溶液にてポリマーを含む。熱いセメント131aは、フラッシュ濃縮器137(ここでは、流れ131a中のヘキサンの一部を気化させることにより、セメントが濃縮される)を通って流れる。フラッシされたヘキサンは、溶液ドラム123へ再循環され、第2図を参照にして以下に記載されているように、フラッシュ濃縮器のアウトプット137bは、ハロゲン化の供給原料である。セメント・ストリッパーからの、塩化メチル、モノマー及び少量のヘキサンの全て131bが、再循環される。151は、乾燥機187、塩化メチル精製塔111、再循環塔183及びパージ塔185と関連して塩化メチル111a及びイソブチレン185aを再循環させる、再循環気体凝縮機である。流れ185bは、このプロセスからパージされる。
第2図に示されたハロゲン化プロセスでは、ブチルゴム溶液137bはタンク153に貯蔵される。その溶液は、塩素又は臭素155と、1つ以上の高度に攪拌された反応容器157において30乃至60℃で反応する。安全上の理由から、塩素は、蒸気又は希釈溶液として導入されるが、これは、液体塩素がブチルゴム溶液と激しく反応するためである。しかし、臭素は、液体又は蒸気の形態で使用され得、これは、その反応速度がより低いためである。ハロゲン化の副産物であるHCl又はHBrは、強度の高い混合器159中で、希釈された水性苛性アルカリ163で中和される。ステアリン酸カルシウム及びエポキシ化大豆油のような抗酸化剤及び安定化剤167が添加される。その溶液は、多容器溶媒除去系171(ここでは、蒸気及び水165が、溶媒を気化し、水中に団粒様ゴム粒子を生じさせる)へ送られる。最終的な溶媒含有量及び溶媒除去のために蒸気を使用することは、各容器の条件による。典型的には、鉛フラッシュドラムは、105乃至120℃及び200乃至300kPa(2乃至3atm)で操作される。最終的なストリッピング工程173での条件は、101℃及び105kPa(1.04atm)である。ヘキサン175aは、再循環され、一方、ハロブチルスラリー173aは、仕上げのために送られる。
第3図は、最終生成物の特性を予測することを可能にする方法での、流動流れ(flow stream)のオンライン監視のための、本発明に有用な装置の例示である。次に、この予測は、所望の特性を有する最終生成物を得るために、処理量及び装置の操作条件をうまく処理するのに使用される。
計装アセンブリー500は、一般に、プロセスの流動流れ中の流体を監視するために備え付けられる。上記のように、本発明の一つの態様では、これは、セメント・ストリッパー131へのアウトプットで、セメント溶液131a(第1図)及びハロゲン化の後のアウトプット157a(第2図)を監視するために行われる。セメント・ストリッパー131のアウトプットでは、測定値は、ムーニー粘度、不飽和度及び分子量分布を決定するために使用され、一方、ハロゲン化の後のアウトプット157a(第2図)では、測定値は、最終生成物のハロゲン含有量を予測するために使用される。図示されているように、プロセス流れ中の流体の流動の方向と共に、計装アセンブリーへの流れは、491で示され、一方、流出は493で示される。本発明の好ましい態様では、アセンブリーは、分光計、粘度計、及び温度測定装置を含む。第3図では、分光計は501にて示されている。好ましい態様では、これは、フーリエ変換近赤外(FTNIR)分光計である。その名が示すように、FTNIRは、近赤外領域で測定を行うために設計された分光計であり、入力されたデータのフーリエ変換を計算するために、適切なマイクロプロセッサー(図示せず)を含む。FTNIR分光計からの繊維−光リンク(fiber optic link)507は、隙間503と505の間の流動流れを横切って赤外信号を送る。FTNIR分光計の出力は、スペクトル・データN(これは、監視された流体の吸収スペクトルを詳細に述べており、下記するように、プロセス制御コンピューター(図示せず)により使用される)である。
計装は、又、509で示される粘度計を含み、これは、流体流動流れ中にプローブ511を有する。プローブは、流動流れ中の流体の粘度積(粘度と密度の積)を測定する。粘度積の測定値は、プロセス制御コンピューター(図示せず)による使用のための、Pでの出力である。
計装の次の要素は、温度測定装置511であり、流動流れ中に流体の温度を監視するプローブ513を含む。温度測定装置の出力は、流動流れ中の流体の温度の温度測定値Oである。温度測定値Oは、下記するように、プロセス制御コンピューター(図示せず)により使用される。
好ましい態様では、赤外信号のための径路長は、約0.8mmである。この径路長は、径路長が更に非常に短い従来の方法に比べ、径路長の変更ために、吸収スペクトルを補正する必要性を大きく減少させる。
計装の要素(温度測定装置、粘度計及び分光計)は、当業者が詳しく知っているであろうから、詳細には議論しない。
計装アセンブリーの出力N、O、及びPが、下記するように、測定値を分析するコンピューターへ送られ、プロセスから予期される最終生成物の特性を予測する。生成物の予測された特性と所望の特性との間の差も又、下記するように、プロセス・パラメータを制御するために使用される。
本明細書に開示した三つの測定装置(分光計、粘度計及び温度ゲージ)は、例示の目的のためのみのものである。当業者であれば、他の測定が行われ得ることを認識するであろう。これらの更なる測定は、本発明の範囲内であることが意図されている。
データの解析
Brown(米国特許第5,121,337号)は、スペクトル測定プロセス自体によるデータについてスペクトル・データを補正し、そのような方法を用いて試料の未知の特性及び/又は組成データを推定する方法を開示している。この特許は、参照により本明細書中に援用され、FTNIR分光計から得られるスペクトル・データの解析の基礎を形成する。
Brownにより開示されたように、解析の第一工程は、較正工程である。n個の較正試料についてのスペクトル・データが、f個の別個の周波数で定量され、較正データの(f×nの次元の)行列Xを生じさせる。この方法の第一工程は、f個の別個の周波数でのm個のデジタル化された補正スペクトルを含むf×mの次元の補正行列Umを生じさせることを包含し、この補正スペクトルは、測定プロセス自体から生じるデータをシミュレートしている。次の工程は、Umに関してXを直交させ、そのスペクトルがUm中のすべてのスペクトルに直交する補正スペクトル行列Xcを生じさせることを包含する。この直交性のため、行列Xc中のスペクトルは、測定プロセス自体から生じるスペクトルから統計的に独立である。
スペクトルは、吸収スペクトルであることができ、以下に記載する好ましい態様は、すべて吸収スペクトルを測定することを包含する。しかし、これは、例示として考えられるべきものであり、添付の請求の範囲により定義される本発明の範囲を制限するものではない。これは、本明細書中で開示される本方法が、反射スペクトル及び散乱スペクトル(例えばラマン散乱)のような他のタイプのスペクトルに適用できるものであるためである。本明細書における記載及び図面への言及は、NIR(近赤外線)及びMIR(中赤外線)に関連するが、それにもかかわらず、本方法には、例えば紫外線、可視分光分析及び核磁気共鳴(NMR)分光分析を含む他のスペクトル測定波長範囲での適用が見出されることが理解されるであろう。
一般に、測定プロセス自体から生じるデータは、二つの影響による。第一は、スペクトルの基線の変動によるものである。基線の変動は、測定中の光源温度変動、反射率、セルのウインドウによる散乱又は吸収、及び機器の検出器の温度(従って感度)変化のような、多数の原因から生じる。これらの基線の変動は、一般に、(広範な周波数範囲にわたって相関する)幅広いスペクトルの特徴を呈する。第二のタイプの測定プロセス信号は、測定プロセスの間に存在する前試料の化学化合物によるものであり、これは、スペクトル中に、よりシャープなラインの特徴を与える。本出願については、このタイプの補正は、一般に、分光計内の大気中の水蒸気及び/又は二酸化炭素による吸収を含む。光繊維中の水酸基による吸収もまた、この様式で処理され得る。試料中に存在する夾雑物についての補正もなされ得るが、これは、一般に、來雑物の濃度が試料成分の濃度を有意に希釈しないように充分に低いものであって、夾雑物と試料成分との間に有意な相互作用が起こらない場合のみである。これらの補正が、試料中の成分によらない信号についてのものであることを認識することは重要である。これに関連して、「試料」とは、モデル開発のためのデータを提供する目的で、その特性及び/又は成分濃度の測定が行われる物質をいう。「夾雑物」により、本発明者等は、特性/成分濃度測定の後であるが、スペクトル測定の前又はその最中に、試料に物理的に添加されたあらゆる物質を指し示す。
本発明を実施する好ましい方法において、補正行列Umに関して直交させられたスペクトル・データの行列Xに加えて、Umのスペクトル又は列が、すべて相互に直交する。相互に直交するスペクトル又は列を有する行列Umの作製は、最初に、一組の直交する周波数(又は波長)依存性多項式によって基線変動をモデル化(これは、基線変動のコンピュータで作製したシミュレーションであり、行列Upを形成する)することによって達成され、その後、少なくとも1つの、そして通常は複数の、機器で集められた実際のスペクトルである前試料の化学化合物(例えば二酸化炭素及び水蒸気)のスペクトルが供給されて、行列Xsを形成する。次に、Xsの列をUpに関して直交させて、新たな行列Xs’を形成する。先の工程は、前試料の化学化合物補正から、基線の影響を除去する。その後、Xs’の列を互いに関して直交させて新たな行列Usを形成し、最後に、Up及びUsを一緒にして、その列が、並べて配置されたUP及びUsの列である補正行列Umを形成する。Xsの列を最初に直交させてベクターの新たな行列を形成し、その後、行列Upを形成する(相互に直交する)多項式をこれらのベクターに関して直交させ、その後、それらと一緒にして補正行列Umを形成するように、工程の順序を変更することが可能であろう。しかし、この変更された順序は、好ましさが劣る。その理由は、それが、最初の段階で直交する多項式を生成する利点を無効にし、また、前試料の化学化合物によるスペクトル変動に基線変動を混入させて、機器の性能の診断として有用性が劣るものにさせるためである。
一旦、行列X(f×nの次元)が補正行列Um(f×mの次元)に関して直交されてしまっても、得られる補正されたスペクトル行列Xcは、なおノイズ・データを含む。このノイズは、以下のようにして除去され得る。最初に、Xc=UVtの形(ここで、Uは、f×nの次元の行列であり、列としての主要な要素のスペクトルを含み、n×nの次元の対角行列であり、特異値を含み、そして、Vは、n×nの次元の行列であり、主要な要素スコアを含み、Vtは、Vの転置である)の行列Xcについて、特異値分解が行われる。一般に、当初のn個の試料におけるスペクトル測定中のノイズに対応する主要な要素は、求めるスペクトル・データによるものに対して大きさの小さい特異値を有し、従って、真の試料成分による主要な要素からは区別され得る。従って、本方法の次の工程は、U及びVからノイズに対応するk+1〜nの主要な要素を除去し、それぞれf×k、k×k及びn×kの次元の新たな行列U’、’、及びV’を形成することを含む。これらの行列を一緒に掛け合わせると、得られる行列は、先に補正されたスペクトル行列Xcに相当し、ノイズによるスペクトル・データを含まない。
モデル中に保持するための主要な要素の数(k)の選択については、文献において示唆されている種々の統計的試験を用いることができるが、以下の工程が最良の結果を与えることが見出された。一般に、スペクトルのノイズ・レベルは、機器を用いる経験から知られる。固有スペクトル(特異値分解から得られる行列Uの列)の視覚検査から、訓練された分光分析者は、一般に、固有スペクトル中の信号レベルがノイズ・レベルにいつ匹敵するかを認識することができる。固有スペクトルの視覚検査により、保持すべき項の適切な数kが選択され得る。次に、モデル中に例えばk−2、k−1、k、k+1、k+2項を有するモデルが構築され得、標準誤差及びPRESS(二乗の予測残余誤差の合計)値が調べられる。次に、モデルにおいて、所望の正確さを得るために必要な項の最小数又は最小のPRESS値を与える項の数が選択される。工程の数の選択は、分光分析者によって行われ、自動化されない。二乗の予測残余誤差の合計は、試料(これは、較正に用いられなかったが、特性又は成分濃度の真の値は既知である)の試験セットについての特性及び/又は成分値の推定のための予測モデルを適用することにより、算出される。推定された値と真の値との間の差を二乗して、試験セット中のすべての試料について合計する(二乗の合計の商の平方根及び試験試料の数は、PRESS値を試料当たりの基準で表すために、しばしば算出される)。PRESS値は、交差確認手順を用いて算出され得る。この手順においては、較正試料の一つ又は二つ以上が、較正の間にデータ行列から除外され、次に、得られたモデルを用いて解析し、この手順を、各試料が1回除外されるまで繰り返す。
本方法は、c個の特性及び/又は組成データを、n個の較正試料の各々について集め、n×c(ここにおいて、cは1である)の次元の行列Yを形成することを、更に必要とする。較正試料の各々について、対応するXcの列は、主要な要素(列の)の重みがつけられた組み合わせにより表される。これらの加重値は、「スコア」と呼ばれ、siで表される。次に、特性(従属変数)と、「スコア」及び他の測定値(独立変数)の組み合わせとの間の回帰関係が決定される。本発明において用いる更なる測定値は、粘度積(粘度及び密度の積、vで表される)及び温度tである。一旦これらの回帰係数が決定されると、これらは、オンライン予測プロセスの一部として用いられる。予測プロセスにおいては、測定されたスペクトルは、上記のようにバックグラウンド効果について補正され、決定された主要な要素を伴う「スコア」が算出される。スコア、測定された粘度積及び温度、ならびに較正プロセスから導かれた回帰係数は、考慮中の特性の予測を与える。
FTNIRスペクトル測定を粘度積及び温度と共に用いて、ムーニー粘度が正確(1単位以内)に予測され得ることが見出された。これは、従来技術に対して重要な改良である。不飽和含有量及びハロゲン含有量は、FTNIRスペクトルとの直接の相関により、正確に予測され得る。
当業界の熟練者は、特異値分解を通じて本発明により得られる固有スペクトルが、用いた波長の範囲について一組の正規直交基底関数を形成することを認識するであろう。基底関数の正規直交セットの任意の要素は、それ自体について1の内積及び基底関数の正規直交セットの他の任意の要素についてはゼロを有する。他の正規直交基底関数もまた、ルジャンドル多項式及び三角関数(サイン及びコサイン)を含む予測モデルを誘導するのに用い得る。他の正規直交基底関数の使用は、本発明の範囲内であるものと意図されている。
第4図は、FTNIRスペクトル測定値ならびに粘度積及び温度の測定値からのムーニー粘度の予測の結果の比較を示す。横座標は、実験室試料の測定されたムーニー粘度であり、一方、縦座標は、回帰相関に基づいて予測されたムーニー粘度である。図からわかるように、適合は、1単位未満の予測の標準誤差しかなく、非常に良好である。
第5図は、不飽和度について、既知の値を有するハロブチルゴムの、スペクトル測定値のみに基づく不飽和度の予測を比較する同様のプロットである。横座標は、ハロブチルゴム不飽和含有量の測定された実験値であり、一方、縦座標は、スペクトル値の回帰からの、予測されたハロブチル不飽和度の値である。
第4図及び第5図において為された予測は、以下に記載するように、これらの特性のフィードバック制御を提供することができるのに十分な程、正確である。
プロセス制御
上記の方法により為されたムーニー粘度のその場(イン−サイツ)での決定は、第1図中の117bでの触媒添加又は共開始剤速度をうまく処理する制御装置への入力として用いられ得る。不飽和度は、第1図中の供給原料107のイソプレン含有量をうまく処理する制御装置への、その場での飽和測定値を用いることにより、制御され得る。コモノマーの組込みは、ブチル反応器の供給原料コモノマー(好ましい態様においてはイソプレンである)107をうまく処理する制御装置への入力として、その場でのコモノマー含有量を用いることにより、制御され得る。分子量分布は、ブチル反応器への冷却流125b及び/又は分岐剤流109bをうまく処理する制御装置への入力として、その場での分子量分布を用いることにより、制御され得る。ブチル反応器ハロゲン含有量は、ブチルハロゲン化反応器へのハロゲン流(第2図中の155)をうまく処理する制御装置への入力として、その場でのハロゲン含有量測定値を用いることにより、制御され得る。
上記の例は、例証のみを目的とするものである。本発明は、広範囲の種々のプロセス及び製造プラントについて用いられ得る。本方法が用いられ得るプロセスは、重合、水蒸気分解、オレフィン精製、芳香族精製、異性化、接触分解、接触改質、水素化、酸化、部分酸化、脱水、水和、ニトロ化、エポキシ化、蒸留、燃焼、アルキル化、中和、アミノ化、エステル化、二量化、膜分離、カルボニル化、ケトン化、ヒドロホルミル化、オリゴマー化、熱分解(pyrolysis)、スルホン化、結晶化、吸着、抽出蒸留、水素化脱アルキル化、脱水素化、芳香族化、環化、熱分解(thermal cracking)、水素化脱硫、水素化脱窒素、過酸化、脱灰及びハロゲン化を包含するが、これらに限定されない。制御される特性ては、ムーニー粘度、ポリマー不飽和度、コモノマーの組込み、ハロゲン含有量、ポリマー濃度、モノマー濃度、分子量、メルト・インデックス、流れ成分組成、生成物中の水分及び分子量分布を包含できる。プロセスに応じて、スペクトル測定値に加えて為されるその場での測定は、中でも、温度、粘度、圧力、密度、屈折率、pH値、コンダクタンス及び比誘電率を包含し得る。
プロセス、制御される特性及び制御される特性の予測のために為される測定におけるこれらの、そして他の同様の変異は、本発明の範囲内であると意図される。Field of Invention
The present invention relates to a chemical plant and a method for controlling a chemical process in the chemical plant. More particularly, the present invention relates to Mooney viscosity, polymer unsaturation, comonomer incorporation, halogen content, molecular weight, and molecular weight distribution during the polymerization or halogenation process of isoolefin copolymers and multiolefins, particularly butyl rubber. It is related with the method of controlling.
Description of related technology
An excellent method for controlling the polymerization of olefins in a medium such as an inert solvent or diluent is to calculate a single variable, the “Mooney viscosity”, the polymer concentration in the medium and the polymer solution. Measuring the viscosity of the. The Mooney viscosity is then used as a single variable that controls the entire process.
"Single variable" process control is very effective when the desired product quality is directly proportional to a single variable. “Single variable” process control is less effective when two or more variables are directly related to product quality. For example, the quality of butyl rubber is directly related to both Mooney viscosity and molecular weight distribution within the polymer solution during processing.
Various attempts have been made to provide further process control for the production of butyl rubber based on the molecular weight distribution of the polymer and Mooney viscosity. Unfortunately, previous methods have been based on comprehensive data that is either inefficient or insufficient to effectively control the process based on molecular weight distribution as well as Mooney viscosity.
There is a need for an efficient and accurate method for controlling the production of butyl rubber, using both Mooney viscosity and polymer molecular weight as process control parameters.
Summary of invention
The present invention is a method for on-line control of a process plant having a plurality of steps for producing a product having a characteristic P having a desired value D. In this method, for a set of calibration samples representing at least one intermediate step in the process, a set of measured spectra is obtained, the effects of measurement errors on the calibration sample are removed, and for a set of calibration samples. Yields a set of corrected spectra. A set of weights is determined by relating the corrected spectrum of each calibration sample to the unique spectrum, giving a matrix of weights. For each calibration sample, a value of the final product characteristic P is obtained. A prediction model is then derived by relating the value of the product characteristic P for the calibration sample to a set of weights. Next, the spectrum of the test sample at an intermediate stage of the process is measured, and the measurement error is corrected. The value of characteristic P for the test sample is predicted from a predictive model that uses a set of weights derived from the calibration sample and the corrected spectrum of the test sample. The difference between the predicted value and the desired value is used to control the process. Optionally, in addition to the spectrum, measurements can be taken and used to derive the prediction model and prediction process.
[Brief description of the drawings]
FIG. 1 is a simplified flow diagram of a plant for the polymerization of butyl rubber.
FIG. 2 is a simplified flow diagram of a plant for halogenation of butyl rubber.
FIG. 3 describes the equipment used for instrumentation and process control.
FIG. 4 is a comparison of Mooney viscosity measured and predicted according to the method of the present invention.
FIG. 5 is a comparison of the degree of unsaturation measured and predicted according to the method of the present invention.
Detailed Description of the Invention
The present invention is best understood with reference to the accompanying FIGS. 1-5 which illustrate preferred embodiments of the invention.
The world production bulk of butyl rubber is produced by a precipitation (slurry) polymerization process in which isobutylene and a small amount of isoprene are copolymerized using aluminum chloride in dilute methyl chloride at -100 ° C to -900 ° C. Halogenated butyl rubber is produced commercially by dissolving butyl rubber in a hydrocarbon solvent and contacting the solution with a halogen element.
FIG. 1 is a simplified flow diagram of the polymerization section of a slurry process. The
In the halogenation process shown in FIG. 2, the
FIG. 3 is an illustration of an apparatus useful in the present invention for on-line monitoring of a flow stream in a manner that allows predicting the properties of the final product. This prediction is then used to successfully process the throughput and equipment operating conditions to obtain a final product with the desired properties.
The
The instrumentation also includes a viscometer, indicated at 509, which has a
The next component of the instrumentation is a
In a preferred embodiment, the path length for infrared signals is about 0.8 mm. This path length greatly reduces the need to correct the absorption spectrum in order to change the path length compared to conventional methods where the path length is much shorter.
Instrumentation elements (temperature measuring devices, viscometers and spectrometers) will not be discussed in detail since those skilled in the art will be familiar with them.
The outputs N, O, and P of the instrument assembly are sent to a computer that analyzes the measurements, as described below, to predict the expected end product characteristics from the process. The difference between the predicted and desired properties of the product is also used to control the process parameters as described below.
The three measuring devices (spectrometer, viscometer and temperature gauge) disclosed herein are for illustrative purposes only. One skilled in the art will recognize that other measurements can be made. These further measurements are intended to be within the scope of the present invention.
Analyzing data
Brown (US Pat. No. 5,121,337) describes a method for correcting spectral data for data from the spectral measurement process itself and using such methods to estimate unknown properties and / or composition data of the sample. Disclosure. This patent is hereby incorporated by reference and forms the basis for the analysis of spectral data obtained from FTNIR spectrometers.
As disclosed by Brown, the first step in the analysis is the calibration step. Spectral data for n calibration samples is quantified at f distinct frequencies, giving rise to a matrix X (of dimensions f × n) of calibration data. The first step of the method involves generating a correction matrix Um of dimension f × m containing m digitized correction spectra at f distinct frequencies, which correction spectrum is measured Simulates data resulting from the process itself. The next step involves orthogonalizing X with respect to Um, resulting in a corrected spectral matrix Xc whose spectrum is orthogonal to all the spectra in Um. Because of this orthogonality, the spectra in matrix Xc are statistically independent of the spectra that result from the measurement process itself.
The spectrum can be an absorption spectrum, and the preferred embodiments described below all involve measuring the absorption spectrum. However, this is to be considered as illustrative and does not limit the scope of the invention as defined by the appended claims. This is because the method disclosed herein can be applied to other types of spectra such as reflection spectra and scattering spectra (eg, Raman scattering). Reference to the description and drawings herein relates to NIR (Near Infrared) and MIR (Mid Infrared), nevertheless, the method includes, for example, ultraviolet, visible spectroscopy and nuclear magnetic resonance (NMR). It will be appreciated that applications in other spectral measurement wavelength ranges will be found, including spectroscopic analysis.
In general, the data resulting from the measurement process itself is due to two effects. The first is due to fluctuations in the baseline of the spectrum. Baseline variations arise from a number of sources, such as source temperature variations during measurement, reflectivity, scattering or absorption by the cell window, and instrument detector temperature (and hence sensitivity) changes. These baseline variations generally exhibit broad spectral features (correlated over a wide frequency range). The second type of measurement process signal is due to the chemical compound of the previous sample present during the measurement process, which gives sharper line features in the spectrum. For this application, this type of correction generally includes absorption by atmospheric water vapor and / or carbon dioxide in the spectrometer. Absorption by hydroxyl groups in the optical fiber can also be treated in this manner. Corrections for contaminants present in the sample can also be made, but generally this is sufficiently low so that the concentration of contaminants does not significantly dilute the concentration of the sample components. Only if no significant interaction occurs between It is important to recognize that these corrections are for signals that do not depend on the components in the sample. In this context, “sample” refers to a substance whose properties and / or component concentrations are measured for the purpose of providing data for model development. By “contaminants” we indicate any material physically added to the sample after the property / component concentration measurement but before or during the spectrum measurement.
In a preferred method of practicing the invention, in addition to the matrix X of spectral data orthogonalized with respect to the correction matrix Um, the spectra or columns of Um are all orthogonal to one another. The creation of a matrix Um with mutually orthogonal spectra or columns involves first modeling baseline variations with a set of orthogonal frequency (or wavelength) dependent polynomials (this is a computer-generated simulation of baseline variations). The spectrum of the chemical compounds (eg carbon dioxide and water vapor) of the previous sample, which is the actual spectrum collected by the instrument, which is then achieved by forming a matrix Up) Are supplied to form the matrix Xs. Next, the columns of Xs are orthogonalized with respect to Up to form a new matrix Xs ′. The previous step removes the influence of the baseline from the chemical compound correction of the previous sample. Thereafter, the columns of Xs ′ are orthogonalized with respect to each other to form a new matrix Us, and finally, Up and Us are combined to form a correction matrix Um whose columns are UP and Us columns arranged side by side. Form. The columns of Xs are first orthogonalized to form a new matrix of vectors, after which the polynomials that form the matrix Up (orthogonal to each other) are orthogonalized with respect to these vectors and then together with them the correction matrix Um It would be possible to change the order of the steps to form However, this changed order is less preferred. The reason is that it negates the advantage of generating an orthogonal polynomial in the first stage, and also introduces baseline fluctuations into the spectral fluctuations due to chemical compounds in the previous sample, making it less useful as a diagnostic of instrument performance. This is to make things happen.
Once the matrix X (dimension of f × n) is orthogonal with respect to the correction matrix Um (dimension of f × m), the resulting corrected spectral matrix Xc still contains noise data. This noise can be removed as follows. First, in the form of Xc = UVt, where U is a matrix of dimensions f × n, containing the spectrum of the main elements as columns, a diagonal matrix of dimensions n × n, and singular values And V is an n × n dimensional matrix, including the main element scores, and Vt is the transpose of V), singular value decomposition is performed. In general, the primary factor corresponding to noise during spectral measurements in the original n samples has a singular value that is small in magnitude relative to that from the desired spectral data, and thus the primary component due to the true sample component. It can be distinguished from the element. Therefore, the next step of the method removes k + 1 to n major elements corresponding to noise from U and V, and creates a new matrix U ′ with dimensions f × k, k × k and n × k, respectively. Forming 'and V'. When these matrices are multiplied together, the resulting matrix corresponds to the previously corrected spectral matrix Xc and does not include spectral data due to noise.
Various statistical tests suggested in the literature can be used to select the number of key elements (k) to keep in the model, but the following steps have been found to give the best results. It was issued. In general, the noise level of a spectrum is known from experience with the instrument. From visual inspection of the eigenspectrum (the column of the matrix U resulting from the singular value decomposition), a trained spectrographer can generally recognize when the signal level in the eigenspectrum is comparable to the noise level. . By visual inspection of the eigenspectrum, an appropriate number k of terms to keep can be selected. Next, a model can be built with, for example, k-2, k-1, k, k + 1, k + 2 terms in the model, and the standard error and PRESS (sum of squared prediction residual errors) values are examined. Next, in the model, the minimum number of terms required to obtain the desired accuracy or the number of terms that gives the minimum PRESS value is selected. The selection of the number of steps is done by the spectrograph and is not automated. The sum of the squared predicted residual errors is due to the estimation of properties and / or component values for the test set of the sample (which was not used for calibration but the true value of the property or component concentration is known) It is calculated by applying the prediction model. The difference between the estimated value and the true value is squared and summed for all samples in the test set (the square root of the quotient of the sum of squares and the number of test samples is the PRESS value per sample Often calculated to represent The PRESS value can be calculated using an intersection validation procedure. In this procedure, one or more of the calibration samples are excluded from the data matrix during calibration and then analyzed using the resulting model and this procedure is excluded once for each sample. Repeat until
The method further comprises collecting c characteristic and / or composition data for each of the n calibration samples to form a matrix Y of dimension n × c (where c is 1), I need. For each calibration sample, the corresponding column of Xc is represented by a weighted combination of the primary elements (of the column). These weights are called “scores” and are represented by si. Next, the regression relationship between the characteristic (dependent variable) and the combination of “score” and other measured values (independent variables) is determined. Further measurements used in the present invention are the viscosity product (product of viscosity and density, expressed as v) and temperature t. Once these regression coefficients are determined, they are used as part of the online prediction process. In the prediction process, the measured spectrum is corrected for background effects as described above, and a “score” with the determined key factors is calculated. The score, measured viscosity product and temperature, and regression coefficients derived from the calibration process give a prediction of the property under consideration.
It has been found that Mooney viscosity can be accurately predicted (within 1 unit) using FTNIR spectral measurements with viscosity product and temperature. This is an important improvement over the prior art. Unsaturated content and halogen content can be accurately predicted by direct correlation with the FTNIR spectrum.
Those skilled in the art will recognize that the eigen spectrum obtained by the present invention through singular value decomposition forms a set of orthonormal basis functions for the range of wavelengths used. Any element of the orthonormal set of basis functions has an inner product of 1 for itself and zero for any other element of the orthonormal set of basis functions. Other orthonormal basis functions can also be used to derive prediction models including Legendre polynomials and trigonometric functions (sine and cosine). The use of other orthonormal basis functions is intended to be within the scope of the present invention.
FIG. 4 shows a comparison of Mooney viscosity prediction results from FTNIR spectral measurements and viscosity product and temperature measurements. The abscissa is the measured Mooney viscosity of the laboratory sample, while the ordinate is the Mooney viscosity predicted based on the regression correlation. As can be seen, the fit is very good with a standard error of prediction of less than one unit.
FIG. 5 is a similar plot comparing unsaturation predictions based solely on spectral measurements for halobutyl rubber having a known value for unsaturation. The abscissa is the measured experimental value of the halobutyl rubber unsaturation content, while the ordinate is the predicted halobutyl unsaturation value from the regression of the spectral values.
The predictions made in FIGS. 4 and 5 are accurate enough to be able to provide feedback control of these characteristics, as described below.
Process control
The in-situ determination of Mooney viscosity made by the above method can be used as input to a controller that successfully handles catalyst addition or co-initiator rates at 117b in FIG. . The degree of unsaturation can be controlled by using in situ saturation measurements to a controller that successfully handles the isoprene content of the
The above example is for illustrative purposes only. The present invention can be used with a wide variety of processes and manufacturing plants. Processes in which this method can be used include polymerization, steam cracking, olefin purification, aromatic purification, isomerization, catalytic cracking, catalytic reforming, hydrogenation, oxidation, partial oxidation, dehydration, hydration, nitration, epoxidation, Distillation, Combustion, Alkylation, Neutralization, Amination, Esterification, Dimerization, Membrane Separation, Carbonylation, Ketonization, Hydroformylation, Oligomerization, Pyrolysis, Sulfonation, Crystallization, Adsorption, Extractive Distillation Including, but not limited to, hydrodealkylation, dehydrogenation, aromatization, cyclization, thermal cracking, hydrodesulfurization, hydrodenitrogenation, peroxidation, deashing and halogenation Not. Controlled properties can include Mooney viscosity, polymer unsaturation, comonomer incorporation, halogen content, polymer concentration, monomer concentration, molecular weight, melt index, flow component composition, moisture and molecular weight distribution in the product . Depending on the process, the in situ measurements made in addition to the spectral measurements can include, among others, temperature, viscosity, pressure, density, refractive index, pH value, conductance and dielectric constant.
These and other similar variations in processes, controlled properties and measurements made for the prediction of controlled properties are intended to be within the scope of the present invention.
Claims (54)
a.前記プロセスの少なくともーつの中間工程の代表的な一組の較正試料のための、測定誤差を有する一組の測定されたスペクトルを得ること;
b.測定プロセス自体から生じるデータであって、校正試料の成分によるものでないデータをシミュレートする一組の補正されるスペクトルを生じさせること;
c.前記一組の補正されるスペクトルに関して前記一組の測定されたスペクトルを直交させることにより、前記測定されたスペクトルを測定誤差について補正し、前記一組の較正試料について、一組の補正されたスペクトルを生じさせること;
d.前記較正試料の各々の前記補正されたスペクトルを一組の正規直交基底関数に関係付けて一組の補正された較正試料スペクトルから一組の加重値を決定すること;
e.前記一組の較正試料の各較正試料に対応する生成物の前記特性Pの値を得ること;
f.前記生成物の前記特性Pについての前記値を前記一組の加重値に関係付けて予測モデルを決定すること;
g.前記プロセスの前記少なくともーつの中間工程において、試験試料のスペクトルを測定すること;
h.前記プロセスの前記少なくともーつの中間工程において、前記一組の補正されるスペクトルに関して前記試験試料の前記測定されたスペクトルを直交させることにより、前記試験試料の補正されたスペクトルを得ること;
i.前記予測モデル及び前記試験試料の前記補正されたスペクトルから、前記試験試料に対応する予測された生成物の前記特性Pについて、推定値Eを決定すること;及び
j.前記予測された生成物の前記推定値Eと前記所望の値Dとの間の算出された差を用いて前記プロセスを制御すること
を含む方法。Online of a process with multiple steps to produce a final product with a characteristic P having a desired value D, where the spectrum of the test sample is measured in an intermediate step of the process and used to control the characteristic P of the product A method for control,
a. Obtaining a set of measured spectra with measurement errors for a representative set of calibration samples of at least one intermediate step of the process;
b. Generating a set of corrected spectra that simulate data that originates from the measurement process itself and not from the components of the calibration sample;
c. Correcting the measured spectrum for measurement error by orthogonalizing the set of measured spectra with respect to the set of corrected spectra, and for the set of calibration samples, a set of corrected spectra Producing;
d. Associating the corrected spectrum of each of the calibration samples with a set of orthonormal basis functions to determine a set of weights from the set of corrected calibration sample spectra;
e. Obtaining a value of the characteristic P of the product corresponding to each calibration sample of the set of calibration samples;
f. Associating the value for the characteristic P of the product with the set of weights to determine a prediction model;
g. Measuring the spectrum of the test sample in the at least one intermediate step of the process;
h. Obtaining a corrected spectrum of the test sample by orthogonalizing the measured spectrum of the test sample with respect to the set of corrected spectra in the at least one intermediate step of the process;
i. Determining an estimate E for the characteristic P of the predicted product corresponding to the test sample from the predicted model and the corrected spectrum of the test sample; and
j. Controlling the process with a calculated difference between the estimated value E of the predicted product and the desired value D.
前記プロセスの前記少なくともーつの中間工程において、前記試験試料の前記少なくともーつの更なる特性を測定すること
を含む、請求項1の方法であって、
前記特性Pについての前記値を、前記一組の加重値及び前記較正試料の前記少なくともーつの更なる特性の前記値に関係付けて予測モデルを決定し、前記予測モデル、前記補正されたスペクトル及び前記試験試料の前記少なくともーつの更なる特性の前記値から、前記試験試料に対応する予測された生成物の前記特性Pについて、前記推定値Eを決定する、方怯。Obtaining at least one additional property value for each calibration sample of the set of calibration samples; and measuring the at least one additional property of the test sample in the at least one intermediate step of the process. The method of claim 1 comprising:
Determining a prediction model by relating the value for the characteristic P to the set of weights and the value of the at least one additional characteristic of the calibration sample, the prediction model, the corrected spectrum, and The method of determining the estimated value E for the property P of the predicted product corresponding to the test sample from the value of the at least one further property of the test sample.
(1)そのプロセスの少なくともーつの中間工程に対応する、測定誤差によって悪影響を及ぼされているスペクトルを測定し、一組の較正試料及び試験試料について一組の測定されたスペクトルを与えるための、第一の装置;
(2)前記較正試料の各々に対応する最終生成物の前記特性Pの前記値を測定するための第二の装置;及び
(i)測定プロセス自体から生じるデータであって、校正試料の成分によるものでないデータをシミュレートする一組の補正されるスペクトルを生じさせること;
(ii)前記一組の補正されるスペクトルに関して前記校正試料及び前記試験試料の測定されたスペクトルを直交させることにより、前記較正試料及び前記試験試料の前記測定されたスペクトルを測定誤差について補正し、それらの組の補正されたスペクトルを与えること;
(iii)前記較正試料の各々の前記補正されたスペクトルを一組の正規直交基底関数に関係付けて一組の補正された較正試料スペクトルから一組の加重値を決定すること;
(iv)前記較正試料について、前記一組の加重値を、前記較正試料の各々に対応する前記生成物の前記特性Pの前記測定値に関係付けて予測モデルを得ること;
(v)前記試験試料に対応する予測された生成物の前記特性Pについての予測値Eを、前記予測モデル及び前記試験試料についての前記補正されたスペクトルから予測すること;及び
(vi)前記予測された生成物の前記予測値Eと前記所望の値Dとの間の算出された差を用いて前記プロセス・プラントを制御すること
に適合されたコンピュータ
を含む、プロセス・プラント。 A process plant having multiple steps to produce a final product with a characteristic P having a desired value D,
(1) measuring a spectrum that is adversely affected by measurement errors, corresponding to at least one intermediate step of the process, to give a set of measured spectra for a set of calibration samples and test samples; First device;
(2) a second device for measuring the value of the characteristic P of the final product corresponding to each of the calibration samples; and (i) data resulting from the measurement process itself, depending on the components of the calibration sample Producing a set of corrected spectra that simulate non-null data ;
(Ii) correcting the measured spectrum of the calibration sample and the test sample for measurement error by orthogonalizing the measured spectrum of the calibration sample and the test sample with respect to the set of corrected spectra ; Giving those sets of corrected spectra;
(Iii) associating the corrected spectrum of each of the calibration samples with a set of orthonormal basis functions to determine a set of weights from the set of corrected calibration sample spectra;
(Iv) for the calibration sample, obtaining the prediction model by relating the set of weights to the measured value of the characteristic P of the product corresponding to each of the calibration samples;
(V) predicting a predicted value E for the characteristic P of the predicted product corresponding to the test sample from the predicted model and the corrected spectrum for the test sample; and
(Vi) a process plant comprising a computer adapted to control the process plant using the calculated difference between the predicted value E and the desired value D of the predicted product .
前記試験試料に対応する予測された生成物の前記特性Pについての予測値Eを、前記予測モデル、前記補正されたスペクトル、及び前記試験試料についての前記少なくともーつの更なる特性の前記値から予測する、プロセス・プラント。33. The process plant of claim 32, further comprising a third device for obtaining a value of at least one additional characteristic of the calibration sample and the test sample, wherein the corrected spectrum and the calibration sample for the calibration sample Associating the value of at least one further characteristic with the measured value of the characteristic P of the product corresponding to each of the calibration samples to obtain a prediction model;
Predicting a predicted value E for the characteristic P of the predicted product corresponding to the test sample from the value of the prediction model, the corrected spectrum, and the at least one additional characteristic for the test sample Process plant.
Applications Claiming Priority (3)
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US3461496P | 1996-12-31 | 1996-12-31 | |
US60/034,614 | 1996-12-31 | ||
PCT/US1997/023734 WO1998029787A1 (en) | 1996-12-31 | 1997-12-31 | On-line control of a chemical process plant |
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JP2001508354A JP2001508354A (en) | 2001-06-26 |
JP2001508354A5 JP2001508354A5 (en) | 2005-09-08 |
JP4435307B2 true JP4435307B2 (en) | 2010-03-17 |
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JP53015698A Expired - Lifetime JP4435307B2 (en) | 1996-12-31 | 1997-12-31 | Online control of chemical process plant |
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US (1) | US6072576A (en) |
EP (1) | EP0948761B2 (en) |
JP (1) | JP4435307B2 (en) |
KR (1) | KR100500802B1 (en) |
CN (1) | CN1105956C (en) |
AU (1) | AU727750B2 (en) |
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CA (1) | CA2276624C (en) |
DE (1) | DE69705323T3 (en) |
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IL (1) | IL130731A (en) |
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NO (1) | NO316346B1 (en) |
PL (1) | PL334397A1 (en) |
RU (1) | RU99116247A (en) |
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- 1998-01-26 TW TW086120089A patent/TW558557B/en not_active IP Right Cessation
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1999
- 1999-06-30 NO NO19993261A patent/NO316346B1/en not_active IP Right Cessation
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US6072576A (en) | 2000-06-06 |
TW558557B (en) | 2003-10-21 |
EP0948761B2 (en) | 2011-09-07 |
WO1998029787A1 (en) | 1998-07-09 |
EP0948761A1 (en) | 1999-10-13 |
CA2276624A1 (en) | 1998-07-09 |
IL130731A0 (en) | 2000-06-01 |
EP0948761B1 (en) | 2001-06-20 |
JP2001508354A (en) | 2001-06-26 |
CN1245570A (en) | 2000-02-23 |
NO316346B1 (en) | 2004-01-12 |
CN1105956C (en) | 2003-04-16 |
DE69705323T3 (en) | 2012-02-09 |
NO993261D0 (en) | 1999-06-30 |
HK1026034A1 (en) | 2000-12-01 |
CA2276624C (en) | 2001-07-24 |
IL130731A (en) | 2004-07-25 |
DE69705323T2 (en) | 2002-04-25 |
PL334397A1 (en) | 2000-02-28 |
KR20000062408A (en) | 2000-10-25 |
AU727750B2 (en) | 2000-12-21 |
BR9714450A (en) | 2000-12-05 |
RU99116247A (en) | 2001-08-20 |
ES2160983T3 (en) | 2001-11-16 |
NO993261L (en) | 1999-08-26 |
DE69705323D1 (en) | 2001-07-26 |
AU5714998A (en) | 1998-07-31 |
ES2160983T5 (en) | 2012-02-15 |
KR100500802B1 (en) | 2005-07-12 |
TWI266769B (en) | 2006-11-21 |
MY117038A (en) | 2004-04-30 |
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